Superpixel image segmentation techniques have been recognized as useful for segmenting digitized images into discrete pixel clusters to aid and enhance further image analysis. The discrete pixel clusters, called superpixels, represent contiguous groups of digital image pixels sharing similar characteristics, such as color, texture, intensity, etc. Because the superpixels of a segmented image represent clusters of pixels sharing similar characteristics, further image processing tasks may be carried out on the superpixels themselves, rather than individual pixels. Thus, superpixel segmentation serves to lower the memory and processing requirements for various image processing tasks, which in turn may permit either greater throughput or more in-depth image analysis. Tasks that may benefit from superpixel segmentation include feature extraction, object recognition, pose estimation, and scene classification tasks.
Conventional superpixel segmentation techniques are not well suited to process images that have a high texture content. Image texture is a function of spatial variation in image pixel intensity. Highly textured images are notable in that the colors are not highly varied, but variations in pixel intensity produce well-defined images. Cloth, tree bark, and grass are examples of highly textured images.
Highly textured images are quite common, and it is therefore desirable to provide a superpixel segmentation method that is capable of generating an accurate superpixel segmentation of a textured image. It is also desirable to provide a superpixel segmentation method that is computationally efficient, and able to produce compact superpixel segmentations. A compact superpixel segmentation is one in which the superpixel size is kept as uniform as possible, and the total number of superpixels remains small.